



def        __a			(   __lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase   )     ->     Any:
					global f  # a global dp table for knapsack
					if f[i][j] < 0:
										if j < wt[i - 1]:
															SCREAMING_SNAKE_CASE							:     Optional[Any]						=				mf_knapsack(i - 1      ,	A_      ,	A_      ,	A_   )
										else:
															SCREAMING_SNAKE_CASE							:     List[str]						=				max(
															    mf_knapsack(i - 1      ,	A_      ,	A_      ,	A_   )      ,	mf_knapsack(i - 1      ,	A_      ,	A_      ,	j - wt[i - 1]   ) + val[i - 1]      ,	)
										SCREAMING_SNAKE_CASE							:     Optional[int]						=				val
					return f[i][j]

def        __a			(   __lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase   )     ->     Union[str, Any]:
					SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				[[0] * (w + 1) for _ in range(n + 1   )]

					for i in range(1      ,	n + 1   ):
										for w_ in range(1      ,	w + 1   ):
															if wt[i - 1] <= w_:
																				SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]]      ,	dp[i - 1][w_]   )
															else:
																				SCREAMING_SNAKE_CASE							:     List[Any]						=				dp[i - 1][w_]

					return dp[n][w_], dp

def        __a			(   __lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase   )     ->     str:





					if not (isinstance(A_      ,	(list, tuple)   ) and isinstance(A_      ,	(list, tuple)   )):
										raise ValueError(
										    'Both the weights and values vectors must be either lists or tuples'   )

					SCREAMING_SNAKE_CASE							:     int						=				len(A_   )
					if num_items != len(A_   ):
										SCREAMING_SNAKE_CASE							:     Optional[Any]						=				(
										    'The number of weights must be the same as the number of values.\n'
										    F'''But got {num_items} weights and {len(A_   )} values'''
										)
										raise ValueError(A_   )
					for i in range(A_   ):
										if not isinstance(wt[i]      ,	A_   ):
															SCREAMING_SNAKE_CASE							:     List[Any]						=				(
															    'All weights must be integers but got weight of '
															    F'''type {type(wt[i]   )} at index {i}'''
															)
															raise TypeError(A_   )

					SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE							:     Any						=				knapsack(A_      ,	A_      ,	A_      ,	A_   )
					SCREAMING_SNAKE_CASE							:     str						=				set()
					_construct_solution(A_      ,	A_      ,	A_      ,	A_      ,	A_   )

					return optimal_val, example_optional_set

def        __a			(   __lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase   )     ->     Tuple:





					if i > 0 and j > 0:
										if dp[i - 1][j] == dp[i][j]:
															_construct_solution(A_      ,	A_      ,	i - 1      ,	A_      ,	A_   )
										else:
															optimal_set.add(A_   )
															_construct_solution(A_      ,	A_      ,	i - 1      ,	j - wt[i - 1]      ,	A_   )


if __name__ == "__main__":
			_lowerCamelCase    :		Union[str, Any]		      =    [3, 2, 4, 4]
			_lowerCamelCase    :		Dict		      =    [4, 3, 2, 3]
			_lowerCamelCase    :		Any		      =    4
			_lowerCamelCase    :		List[str]		      =    6
			_lowerCamelCase    :		Optional[Any]		      =    [[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
			_lowerCamelCase    :		int		      =    knapsack(w, wt, val, n)
			print(optimal_solution)
			print(mf_knapsack(n, wt, val, w))  # switched the n and w

			# testing the dynamic programming problem with example
			# the optimal subset for the above example are items 3 and 4
			_lowerCamelCase    :		str		      =    knapsack_with_example_solution(w, wt, val)
			assert optimal_solution == 8
			assert optimal_subset == {3, 4}
			print("""optimal_value = """, optimal_solution)
			print("""An optimal subset corresponding to the optimal value""", optimal_subset)

import warnings

from ...utils import logging
from .image_processing_beit import BeitImageProcessor


__lowerCAmelCase     :	int       			=  logging.get_logger(__name__)




class 			UpperCAmelCase_     (					_A	):


  '''simple docstring'''







  def __init__(					self     :					int     ,				*UpperCamelCase__     :					Optional[Any]     ,				**UpperCamelCase__     :					Optional[Any]       )     ->      None:







    """simple docstring"""






    warnings.warn(
        """The class BeitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
        """ use BeitImageProcessor instead."""     ,				UpperCamelCase__     ,				)
    super().__init__(*UpperCamelCase__     ,				**UpperCamelCase__       )

